Thanks to my work with Thesis, I have the opportunity to regularly audit paid search accounts from various ecommerce/D2C and lead gen brands. Over the years, I've developed a few quick reports that I use to gauge account health & expansion opportunities. In what I hope will be a multi-part blog post series, I plan to share a few of those reports and how I interpret them. I always start with...
I ground my thinking about paid search accounts in Search Term reporting, as opposed to Search Keyword reporting. Search terms are the words being typed by real people into their search engine of choice. Our objective as search marketers is to answer those queries as best we can (in the hopes of driving conversions). Keyword reporting is also critical to understanding account health, but I've found that keyword based analysis can sometimes obfuscate the actual dynamics of the campaigns.
For example, if a broad keyword for sandals is matching to search terms including shoe, espadrille, and slipper, then the CAC & impression share for the sandals keyword may not tell the full story. In this case, we'd really rather understand the CAC & impression share for each of the underlying search terms. If we see there are clear CAC or impression differences between those terms, we might take some action, including restructuring our campaigns to more directly attack each of those terms (with specific ad copy, landing pages, ad extensions etc). If we were to solely look at the keyword reporting, we might make simple bid or match type adjustments and then move on with our day.
Our goal of this analysis is to find how many terms are driving conversions and how many terms are not. Further, we want to see how much spend is being allocated on a percentile basis to terms that are not driving conversions. If that percentile is high, it implies that we could likely cull a number of search terms and/or attack those non-converting terms with a more targeted campaign + ad group + ad strategy.
The output from this analysis looks like this:
In the above table, we are grouping the metrics (notably spend & count of search terms) into buckets based on the number of total conversions attributed (buckets in this case are 0, 0-1, 2-10, 11-50, or >50). For this account, ~61% of spend was allocated to terms that did not drive any conversions, and those terms constituted nearly 97% of the search terms. In other words, 111 total unique terms accounted for all of the conversions driven.
Next steps from this sort of analysis always vary, but in this case our plan was to:
A) Make sure we are doing an excellent job in terms of account restructure, match type, ad copy, ad extensions, and landing pages for our critical 111 terms.
B) Start cutting keywords & terms to drop the 61% of spend figure to something more reasonable.
1) Create a Custom Report for any duration (usually we look at either the last 90 or 365 days, depending on the size of the account).
2) In your report, specify Search term as the Row. We typically add columns for Cost, Clicks, Impressions, CTR, Avg. CPC, Conversions, Conversion Rate, and Cost per Conversion.
3) Export the output into a Google Sheet or Excel doc. I use basic, SUMIFS & COUNTIFS functions to do the calculations. For example, if column B is Cost, and column H is Conversions, S1 is the lowest number in your conversion count grouping and S2 is the highest number in your conversion count grouping, then you could write your SUMIFS Google Sheets equation as follows:
If you are interested in getting a sample sheet for this sort of analysis that is pre-populated with these equations, simply email us at hello @ thesistesting.com.
For good measure, here are a few more examples of this sort of analysis:
In a follow up post, I'll do a broader analysis of 50+ ad accounts and report the averages, but for the time being, my sense is that healthy accounts often have the % of spend allocated to 0 conversions terms between ~35%-50%. You'd certainly never expect that number to be 0 (if it were at 0% your account is WAY too conservative!) but as a rule of thumb when I see that number at >70% there is usually a lot of room for improvement in an account.
Branded search terms have an uncanny knack for sneaking into what are otherwise intended to be non-branded campaigns, especially when you aren't on top of your game with your negative keywords. For that reason, when I'm analyzing search term data I often add a simple brand check.
In Google Sheets, I use a basic =FIND function to search each search term for a full or partial brand name. For example, assuming search terms are in column A, for the brand "livingsocial" I would use the formula =FIND(A1,"living"). Anywhere FIND returns an integer is likely to be a branded keyword! I use the same SUMIFS logic to summarize performance for brand vs. non-brand.
It's not perfect, but it's a quick and easy way to get a read on account wide non-brand and branded CACs. Here is what that output looks like:
PS: I take a lot of inspiration for the above from the Lin-Rodnitzky Ratio which was conceptualized by the founders of 3Q Digital, Will Lin and David Rodnitzky (who is my hero). Their metric is also very much worth checking out as it provides an even more concise overview of account health.
PS2: If you have any feedback (especially critical feedback) on this approach I'd love to hear it!
We've seen surprisingly strong performance using local TV news content in our online paid ads. In this post, I provided an overview of local TV lifestyle programming and how we use that content to drive paid performance.
We (along with the entire industry as far as I know) have seen Facebook's performance decline since Apple's introduction of ATT. Over the last 12 months, we've made channel exploration and expansion a core focus.
Our friends at Nest Commerce recently published their Readout for Jan 2023. In it, they discuss a number of trends they see impacting D2C. Their graphs comparing 2021 and 2022 performance on Black Friday caught my eye as they saw a considerable improvement...